Statistics > Machine Learning

Abstract: Network models have been popular for modeling and representing complex
relationships and dependencies between observed variables. When data comes from
a dynamic stochastic process, a single static network model cannot adequately
capture transient dependencies, such as, gene regulatory dependencies
throughout a developmental cycle of an organism. Kolar et al (2010b) proposed a
method based on kernel-smoothing l1-penalized logistic regression for
estimating time-varying networks from nodal observations collected from a
time-series of observational data. In this paper, we establish conditions under
which the proposed method consistently recovers the structure of a time-varying
network. This work complements previous empirical findings by providing sound
theoretical guarantees for the proposed estimation procedure. For completeness,
we include numerical simulations in the paper.